ANALYSING TIME SERIES DATA USING EVIEWS: A CASE FOR SINGLE

EQUATION MODEL.

January 20th, 2018/ Kuranga Abdulazeez/ Gifted Hands Associate/ 09038520999

A single equation model is a model such that there is only one direction of causality

between the dependent and the independent variable. This means that in a single

equation model, the independent variable affects the dependent variable but

the dependent variable does not in any way have any effect on the

independent variable.

Time series data have some kind of characteristics which make them to be unique, of

which their ability to have a time trend as well as having mean and variance that are

not constant. Therefore, in modelling time series data, there are certain possible

scenarios which one faces:

1. Series have no time trend and are stationary. This means that the time

series data is not moving at all i.e increasing or decreasing, but the mean and

variance are constant over time. This is a very rare case because it is almost

impossible to have variables that are not moving over time and yet the mean or

variance will be constant.

2. Series are trending but stationary. In this case, it means the data are either

moving upward or downward (which can also be a random movement), yet the

mean and variance are constant. This is also a very rare case.

3. Series are trending, not stationary and not co-integrated. In this case, it

means that the data is either increasing or decreasing overtime, the mean and

variance are not constant. They also do not have relationship either in the long

run or in the short run. This is a possible scenario because it is expected that

once a variable is trending, then the mean and the variance will not be constant

over time.

4. However, the very possible case to encounter for single equation model is when

the series are trending and not stationary but they are co integrated.

This means that apart from the data following the normal way of trending and

not having constant mean and variance, they also have long run relationships.

STEP BY STEP METHOD OF ANALYSIS

1. Import the excel file into Eviews. Click on finish while making sure that the data

is well arranged.

Figure 1

When you click on the ‘finish’ button, it will bring the image below:

Figure 2

2. Double click on each variable to see that the data is the same with the one

in your excel file. To view the trend, click on the variable you want: VIEW ----

GRAPH.

Figure 3

When you click on ‘Graph’, the dialogue box below will appear, and you should

make sure to click ‘OK’ after you have chosen the kind of graph you want:

Figure 4

You can also check the graphical illustration in Excel by highlighting the data on

the variable you want and press ALT+F1.

To view if it has unit root, that is, if the mean and variance are non-stationary:

VIEW --- UNIT ROOT. That is, you click on view (as displayed in figure 3), and

then go down to pick ‘unit root test’ as an option. Here, a dialogue box will appear

as shown below: Figure 5

First of all test at level while including trend in your equation. In the result, check

the P-value. If it is greater than any of the chosen level of significant, you go back

to test at first difference. If it is also not significant. Then you will have to create

a log of that variable (Another situation in which you can also log is

when the variable values are probably huge or you want to change the

variables to percentage). To log in eviews: genr l(variable)= log

(variable). Then click on the enter button. Then you will go back testing the

variable for unit root normally. For example, in the data I used, GDPI was not

significant at level and first difference, so I logged it as shown below:

Figure 6

From figure 6 above, it can be seen that after entering the command for logging a

variable, another variable ‘lgdpi’ was created. And by testing for the unit root of

this, it was seen to be significant at first difference as seen in figure 7 below:

Figure 7

So the number of times you difference your variable to let it be stationary will

determine how you will call the variable. Therefore, if you test for the unit

root at level and it becomes stationary, it will be referred to as I(0). If

differenced at first difference, then it will be referred to as I(1) and so

on. Also, if your variable becomes stationary when you log it, then it is

the log of that variable that will be included in your model and when

you are interpreting, you interpret in percentages.

3. If after testing for the unit root and the variables are all I(0) series, then you run

normal OLS with de-trending.

4. If after testing for unit root and all the variables are I(1) series, then you run a

co integrating test using Engel and granger to determine the long run

relationship among the variables. To run Engel-Granger test: first highlight the

variables (while letting the dependent variable come first), then right click and

open as a group as shown in figure 8 below:

Figure 8

Click on View, go down to where ‘co integrating test’ is. Click on it and then click on

‘single equation co integration test’ as shown in figure 9 below:

Figure 9

NOTE: This is where most researchers make mistakes when analysing

time series data. You only use ‘Johansen system Co-integration test’ for

systems of equation and not for single equation models. The single

equation co-integration test is used for single equation models. Therefore,

if your model is such that only the independent variables have effect on

the dependent variable while the dependent variable does not affect the

independent variable both in theory and common sense, then the single

equation co integration test is what you should use as your test for

relationship among the variables.

Continuing from figure 9, once you click on the Single equation co integration test, a

dialogue box will appear as in figure 10 below:

Figure 10

Change the ‘constant trend’ to ‘linear trend’ and then click on OK (that is, VIEW- CO

INTEGRATING TEST- SINGLE EQUATION CO INTEGRATING TEST- OK.

The decision rule is that if at least one of the variables is significant, it indicates that

there is co integration among the variables. Therefore, you either run A Fully Modified

Ordinary Least Square (FMOLS) or Short run Error Correction Model (ECM).

To run FMOLS, highlight the variables (while letting the dependent variable

come first), right click and open as an equation. A dialogue box will appear,

change the option to ‘co integrating regression’ (as shown in figure 11) after which

another dialogue will appear as shown in figure 12.

Figure 11

Figure 12

Change the constant (level) to linear trend because we are adjusting for time trend.

Then click on OK. The output will be shown as the one in figure 13 below, and you can

then interpret accordingly.

Figure 13

5. If after testing for unit root and some of the variables are I(0) while others are

I(1), the best method of analysis is the bounds test, even though FMOLS is

also good. So any of the method can be determined by your supervisor or what

you like.

PERFORMING BOUNDS TEST

1. Highlight the variables. Make sure your dependent variable comes first

2. Open as equation and change the option to the last one which is Auto-

Regressive Distributive Lag Models (ARDL) and then click OK.

3. Another dialogue box will open. Adjust the lag to suit your taste and then click

OK.

4. From the result that will be shown, click on VIEW. Then COEFFICIENT

DIAGNOSTIC and also BOUNDS TEST (VIEW- COEFFICIENT

DIAGNOSTIC- BOUNDS TEST)

5. If F statistic is greater than the upper and lower bound, then there is co

integration. Next thing to do here is just to run your final analysis which will be

to either run FMOLS or co integration long run as well as short run coefficient.

To do this, click on VIEW- COEFFICIENT DIAGNOSTIC- CO

INTEGRATION AND LONG RUN FORM.

Then you results will show like the figure below and interpret your results accordingly.

6. If F statistic is less than the upper and lower bound, then there is no co-

integration. What to do here is to click on ESTIMATE. A dialogue box will

appear as shown below:

Add D in front of each of the variables and bracket each of them (do not bracket the D)

as shown below: Interpret your result.

7. If F statistic is in between (less than the upper bound and greater

than the lower bound), then it is in conclusive. What you will do in this

case is to go back to adjust your lag until the F statistic is either greater than the

upper and lower bound or less than the upper and lower bound. Then, apply

the appropriate method of analysis.

REFERENCES

Kilishi A.A (2015). Modeling With Time Series: Issues and Common Errors.

Department of Economics, University of Ilorin, Ilorin, Kwara State.

Gujarati N.D and Porter D.C (2009). Basic Econometrics, New York: McGraw Hill.